





"""simple docstring"""





import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict


@pytest.mark.parametrize(
    """split_dict"""				,		[
        SplitDict(),
        SplitDict({"""train""": SplitInfo(name="""train"""				,		num_bytes=13_37				,		num_examples=42				,		dataset_name="""my_dataset"""     )}     ),
        SplitDict({"""train""": SplitInfo(name="""train"""				,		num_bytes=13_37				,		num_examples=42     )}     ),
        SplitDict({"""train""": SplitInfo()}     ),
    ]				,		)
def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		SplitDict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			split_dict._to_yaml_list()
      assert len(SCREAMING_SNAKE_CASE     ) == len(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE     )
      for split_name, split_info in split_dict.items():
            # dataset_name field is deprecated, and is therefore not part of the YAML dump
            lowerCAmelCase    		=			None
            # the split name of split_dict takes over the name of the split info object
            lowerCAmelCase    		=			split_name
      assert split_dict == reloaded






@pytest.mark.parametrize(
    """split_info"""				,		[SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE     ), SplitInfo(dataset_name="""my_dataset"""     )]     )
def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			asdict(SplitDict({"""train""": split_info}     )     )
      assert "dataset_name" in split_dict_asdict["train"]
      assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name













"""simple docstring"""





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


SCREAMING_SNAKE_CASE__   =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       SCREAMING_SNAKE_CASE__   =  json.load(f)

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            return FSMTTokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			FSMTForConditionalGeneration.from_pretrained(lowercase					).to(lowercase					)
            if torch_device == "cuda":
                  model.half()
            return model





      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]					)
      @slow
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Dict:
            # note: this test is not testing the best performance since it only evals a small batch
            # but it should be enough to detect a regression in the output quality
            lowerCAmelCase    		=			f'facebook/wmt19-{pair}'
            lowerCAmelCase    		=			self.get_tokenizer(lowercase					)
            lowerCAmelCase    		=			self.get_model(lowercase					)

            lowerCAmelCase    		=			bleu_data[pair]["""src"""]
            lowerCAmelCase    		=			bleu_data[pair]["""tgt"""]

            lowerCAmelCase    		=			tokenizer(lowercase ,		return_tensors="""pt""" ,		truncation=lowercase ,		padding="""longest"""					).to(lowercase					)
            lowerCAmelCase    		=			model.generate(
                input_ids=batch.input_ids ,		num_beams=8 ,		)
            lowerCAmelCase    		=			tokenizer.batch_decode(
                lowercase ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase					)
            lowerCAmelCase    		=			calculate_bleu(lowercase ,		lowercase					)
            print(lowercase					)
            self.assertGreaterEqual(scores["""bleu"""] ,		lowercase					)






